Abstract
In this paper, we investigate the usefulness of deep links for improving video search results. Deep links are time-coded comments with which viewers express their reactions to the content at specific time-points of a video that they find noteworthy. The rationale underlying our work is that deep links can open up an interesting new perspective on the relevance of a video, namely focusing on individual video segments, in addition to the existing ones that typically concern a video as a whole. In this perspective, deep-link comments provide non-linear access to videos via their time-codes, which can match alternate dimensions of user needs that extend beyond topical and affective relevance. We explore the different types of deep-link comments and develop a viewer expressive reaction variety (VERV) typology that captures how viewers deep-link on YouTube. We validate this typology through a user study on Amazon Mechanical Turk to show that it is a typology human annotators can agree upon. We then demonstrate, through experiments, that deep-link comments can automatically be classified into VERV categories and show the potential of our proposed usage of deep-link comments for video search through a user study.
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